Dividends Stocks

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import math

import warnings
warnings.filterwarnings("ignore")

# yahoo finance data 
import yfinance as yf
yf.pdr_override()

# input
# Online Gaming
title = "Dividends"
symbols = ['BMY', 'MED', 'ET', 'BTI', 'ABBV', 'T', 'DFS', 'JNJ', 'BEP', 'PEAK', 'DUK', 'NTES', 'INTC', 'MCD', 'NVS']
start = '2016-01-01'
end = '2020-06-26'

df = pd.DataFrame()
for s in symbols:
    df[s] = yf.download(s,start,end)['Adj Close']

[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed

from datetime import datetime
from dateutil import relativedelta

d1 = datetime.strptime(start, "%Y-%m-%d")
d2 = datetime.strptime(end, "%Y-%m-%d")
delta = relativedelta.relativedelta(d2,d1)
print('How many years of investing?')
print('%s years' % delta.years)

How many years of investing?
4 years

number_of_years = delta.years

days = (df.index[-1] - df.index[0]).days
days

1634

df.head()

	BMY	MED	ET	BTI	ABBV	T	DFS	JNJ	BEP	PEAK	DUK	NTES	INTC	MCD	NVS
Date
2016-01-04	58.966171	25.097445	9.600537	42.963787	46.704380	26.815956	47.670143	88.997742	18.515593	27.372549	58.656158	159.800079	30.134039	104.540825	63.856762
2016-01-05	60.127369	25.483688	9.539128	43.054810	46.509811	27.003315	47.561607	89.369743	18.682926	27.966824	59.076370	161.415497	29.992189	105.981186	64.199318
2016-01-06	59.071739	25.782158	8.140328	42.967743	46.517910	26.963728	46.630093	88.918037	18.428286	26.842705	59.208195	161.239090	29.327276	105.269913	63.320580
2016-01-07	57.435497	24.983324	7.205522	41.998138	46.380096	26.528322	45.761887	87.881737	17.417028	26.126715	58.664391	153.533188	28.227942	102.833771	62.508881
2016-01-08	55.975193	24.526846	7.382930	41.424274	45.115398	26.552073	45.192127	86.942871	17.162388	26.055111	58.532570	151.759918	27.935379	102.673721	60.736530

df.tail()

	BMY	MED	ET	BTI	ABBV	T	DFS	JNJ	BEP	PEAK	DUK	NTES	INTC	MCD	NVS
Date
2020-06-19	55.982868	117.700676	7.97	39.169998	96.709999	30.309999	53.730000	143.830002	48.970001	26.320000	82.110001	413.209991	59.619999	186.559998	91.099998
2020-06-22	57.124161	119.227448	7.93	39.709999	97.269997	30.110001	54.020000	143.389999	49.570000	26.870001	82.529999	418.200012	60.090000	187.460007	90.620003
2020-06-23	57.937950	124.878510	7.88	39.680000	97.309998	30.250000	54.860001	142.860001	48.980000	26.969999	81.849998	435.070007	59.919998	186.619995	90.459999
2020-06-24	57.322643	121.953842	7.48	38.139999	95.139999	29.420000	50.810001	139.820007	47.580002	25.990000	80.470001	437.459991	59.090000	184.289993	88.800003
2020-06-25	57.739464	129.924805	7.41	39.310001	96.989998	29.719999	52.060001	139.669998	48.290001	26.590000	79.449997	436.059998	58.509998	182.759995	89.599998

plt.figure(figsize=(12,8))
plt.plot(df)
plt.title(title + ' Closing Price')
plt.legend(labels=df.columns)

<matplotlib.legend.Legend at 0x282f372ab00>

# Normalize the data
normalize = (df - df.min())/ (df.max() - df.min())

plt.figure(figsize=(18,12))
plt.plot(normalize)
plt.title(title + ' Stocks Normalize')
plt.legend(labels=normalize.columns)

<matplotlib.legend.Legend at 0x282f37636a0>

stock_rets = df.pct_change().dropna()

plt.figure(figsize=(12,8))
plt.plot(stock_rets)
plt.title(title + ' Stocks Returns')
plt.legend(labels=stock_rets.columns)

<matplotlib.legend.Legend at 0x282f37e37b8>

plt.figure(figsize=(12,8))
plt.plot(stock_rets.cumsum())
plt.title(title + ' Stocks Returns Cumulative Sum')
plt.legend(labels=stock_rets.columns)

<matplotlib.legend.Legend at 0x282f39c2048>

sns.set(style='ticks')
ax = sns.pairplot(stock_rets, diag_kind='hist')

nplot = len(stock_rets.columns)
for i in range(nplot) :
    for j in range(nplot) :
        ax.axes[i, j].locator_params(axis='x', nbins=6, tight=True)

ax = sns.PairGrid(stock_rets)
ax.map_upper(plt.scatter, color='purple')
ax.map_lower(sns.kdeplot, color='blue')
ax.map_diag(plt.hist, bins=30)
for i in range(nplot) :
    for j in range(nplot) :
        ax.axes[i, j].locator_params(axis='x', nbins=6, tight=True)

plt.figure(figsize=(10,10))
corr = stock_rets.corr()

# plot the heatmap
sns.heatmap(corr, 
        xticklabels=corr.columns,
        yticklabels=corr.columns,
            cmap="Reds")

<matplotlib.axes._subplots.AxesSubplot at 0x282f06d8ac8>

# Box plot
stock_rets.plot(kind='box',figsize=(24,8))

<matplotlib.axes._subplots.AxesSubplot at 0x282fd2dce48>

rets = stock_rets.dropna()

plt.figure(figsize=(16,8))
plt.scatter(rets.std(), rets.mean(),alpha = 0.5)

plt.title('Stocks Risk & Returns')
plt.xlabel('Risk')
plt.ylabel('Expected Returns')
plt.grid(which='major')

for label, x, y in zip(rets.columns, rets.std(), rets.mean()):
    plt.annotate(
        label, 
        xy = (x, y), xytext = (50, 50),
        textcoords = 'offset points', ha = 'right', va = 'bottom',
        arrowprops = dict(arrowstyle = '-', connectionstyle = 'arc3,rad=-0.3'))

rets = stock_rets.dropna()
area = np.pi*20.0

sns.set(style='darkgrid')
plt.figure(figsize=(16,8))
plt.scatter(rets.std(), rets.mean(), s=area)
plt.xlabel("Risk", fontsize=15)
plt.ylabel("Expected Return", fontsize=15)
plt.title("Return vs. Risk for Stocks", fontsize=20)

for label, x, y in zip(rets.columns, rets.std(), rets.mean()) : 
    plt.annotate(label, xy=(x,y), xytext=(50, 0), textcoords='offset points',
                arrowprops=dict(arrowstyle='-', connectionstyle='bar,angle=180,fraction=-0.2'),
                bbox=dict(boxstyle="round", fc="w"))

def annual_risk_return(stock_rets):
    tradeoff = stock_rets.agg(["mean", "std"]).T
    tradeoff.columns = ["Return", "Risk"]
    tradeoff.Return = tradeoff.Return*252
    tradeoff.Risk = tradeoff.Risk * np.sqrt(252)
    return tradeoff

tradeoff = annual_risk_return(stock_rets)
tradeoff

	Return	Risk
BMY	0.032710	0.270447
MED	0.486789	0.486225
ET	0.111224	0.570211
BTI	0.016424	0.269174
ABBV	0.207893	0.296661
T	0.050530	0.234043
DFS	0.110700	0.421576
JNJ	0.121264	0.202031
BEP	0.253928	0.281216
PEAK	0.051073	0.335242
DUK	0.093416	0.226076
NTES	0.306611	0.405058
INTC	0.202170	0.327964
MCD	0.152898	0.236772
NVS	0.096132	0.201556

import itertools

colors = itertools.cycle(["r", "b", "g"])

tradeoff.plot(x = "Risk", y = "Return", kind = "scatter", figsize = (13,9), s = 20, fontsize = 15, c='g')
for i in tradeoff.index:
    plt.annotate(i, xy=(tradeoff.loc[i, "Risk"]+0.002, tradeoff.loc[i, "Return"]+0.002), size = 15)
plt.xlabel("Annual Risk", fontsize = 15)
plt.ylabel("Annual Return", fontsize = 15)
plt.title("Return vs. Risk for " + title + " Stocks", fontsize = 20)
plt.show()

rest_rets = rets.corr()
pair_value = rest_rets.abs().unstack()
pair_value.sort_values(ascending = False)

NVS   NVS     1.000000
MCD   MCD     1.000000
MED   MED     1.000000
ET    ET      1.000000
BTI   BTI     1.000000
ABBV  ABBV    1.000000
T     T       1.000000
DFS   DFS     1.000000
BEP   BEP     1.000000
PEAK  PEAK    1.000000
DUK   DUK     1.000000
NTES  NTES    1.000000
INTC  INTC    1.000000
JNJ   JNJ     1.000000
BMY   BMY     1.000000
DUK   PEAK    0.615049
PEAK  DUK     0.615049
MCD   DFS     0.584761
DFS   MCD     0.584761
      T       0.538436
T     DFS     0.538436
NVS   JNJ     0.532011
JNJ   NVS     0.532011
T     DUK     0.528277
DUK   T       0.528277
PEAK  DFS     0.522560
DFS   PEAK    0.522560
DUK   BEP     0.517638
BEP   DUK     0.517638
DUK   JNJ     0.515478
                ...   
NTES  MED     0.198824
MED   NTES    0.198824
      BTI     0.185314
BTI   MED     0.185314
JNJ   MED     0.184951
MED   JNJ     0.184951
T     NTES    0.181377
NTES  T       0.181377
MED   BMY     0.176019
BMY   MED     0.176019
MED   NVS     0.175026
NVS   MED     0.175026
MED   DUK     0.170286
DUK   MED     0.170286
MED   ABBV    0.169227
ABBV  MED     0.169227
JNJ   ET      0.167954
ET    JNJ     0.167954
NTES  PEAK    0.157801
PEAK  NTES    0.157801
ET    BMY     0.156688
BMY   ET      0.156688
BEP   NTES    0.153414
NTES  BEP     0.153414
MED   ET      0.148256
ET    MED     0.148256
DUK   NTES    0.135846
NTES  DUK     0.135846
DUK   ET      0.132593
ET    DUK     0.132593
Length: 225, dtype: float64

# Normalized Returns Data
Normalized_Value = ((rets[:] - rets[:].min()) /(rets[:].max() - rets[:].min()))
Normalized_Value.head()

	BMY	MED	ET	BTI	ABBV	T	DFS	JNJ	BEP	PEAK	DUK	NTES	INTC	MCD	NVS
Date
2016-01-05	0.812202	0.507112	0.544536	0.455302	0.527513	0.515990	0.496509	0.579734	0.490655	0.662871	0.513318	0.547433	0.467771	0.507434	0.598767
2016-01-06	0.643698	0.500935	0.359797	0.434527	0.541971	0.472109	0.464210	0.528534	0.417512	0.498766	0.492596	0.508655	0.421283	0.447172	0.488666
2016-01-07	0.597816	0.429222	0.401689	0.331459	0.531522	0.395893	0.466014	0.491939	0.284418	0.534609	0.444635	0.346997	0.380510	0.398848	0.493686
2016-01-08	0.608103	0.450574	0.585396	0.376120	0.450557	0.484367	0.477524	0.497324	0.414315	0.598053	0.473781	0.472456	0.452711	0.462334	0.403912
2016-01-11	0.676908	0.569031	0.488437	0.524241	0.435441	0.543179	0.520176	0.523231	0.471068	0.646842	0.529940	0.413538	0.526771	0.497728	0.573435

Normalized_Value.corr()

	BMY	MED	ET	BTI	ABBV	T	DFS	JNJ	BEP	PEAK	DUK	NTES	INTC	MCD	NVS
BMY	1.000000	0.176019	0.156688	0.274713	0.428229	0.315677	0.329028	0.433536	0.285307	0.266100	0.294208	0.202021	0.344809	0.307445	0.417191
MED	0.176019	1.000000	0.148256	0.185314	0.169227	0.221331	0.341120	0.184951	0.203276	0.242135	0.170286	0.198824	0.261545	0.277890	0.175026
ET	0.156688	0.148256	1.000000	0.215051	0.239781	0.225168	0.347858	0.167954	0.281581	0.226392	0.132593	0.214664	0.266627	0.257210	0.284873
BTI	0.274713	0.185314	0.215051	1.000000	0.278377	0.400323	0.375725	0.398718	0.383593	0.324905	0.399913	0.225768	0.353805	0.377765	0.423170
ABBV	0.428229	0.169227	0.239781	0.278377	1.000000	0.344304	0.366218	0.429556	0.315016	0.291593	0.264189	0.233399	0.431927	0.331932	0.465002
T	0.315677	0.221331	0.225168	0.400323	0.344304	1.000000	0.538436	0.486676	0.420033	0.459280	0.528277	0.181377	0.428237	0.413158	0.420342
DFS	0.329028	0.341120	0.347858	0.375725	0.366218	0.538436	1.000000	0.374964	0.470334	0.522560	0.459353	0.256752	0.488283	0.584761	0.397743
JNJ	0.433536	0.184951	0.167954	0.398718	0.429556	0.486676	0.374964	1.000000	0.383020	0.333879	0.515478	0.210772	0.448071	0.433855	0.532011
BEP	0.285307	0.203276	0.281581	0.383593	0.315016	0.420033	0.470334	0.383020	1.000000	0.513561	0.517638	0.153414	0.330229	0.492364	0.415699
PEAK	0.266100	0.242135	0.226392	0.324905	0.291593	0.459280	0.522560	0.333879	0.513561	1.000000	0.615049	0.157801	0.358384	0.492864	0.376026
DUK	0.294208	0.170286	0.132593	0.399913	0.264189	0.528277	0.459353	0.515478	0.517638	0.615049	1.000000	0.135846	0.372912	0.506710	0.390120
NTES	0.202021	0.198824	0.214664	0.225768	0.233399	0.181377	0.256752	0.210772	0.153414	0.157801	0.135846	1.000000	0.357841	0.226957	0.296903
INTC	0.344809	0.261545	0.266627	0.353805	0.431927	0.428237	0.488283	0.448071	0.330229	0.358384	0.372912	0.357841	1.000000	0.401970	0.451947
MCD	0.307445	0.277890	0.257210	0.377765	0.331932	0.413158	0.584761	0.433855	0.492364	0.492864	0.506710	0.226957	0.401970	1.000000	0.416880
NVS	0.417191	0.175026	0.284873	0.423170	0.465002	0.420342	0.397743	0.532011	0.415699	0.376026	0.390120	0.296903	0.451947	0.416880	1.000000

normalized_rets = Normalized_Value.corr()
normalized_pair_value = normalized_rets.abs().unstack()
normalized_pair_value.sort_values(ascending = False)

NVS   NVS     1.000000
MCD   MCD     1.000000
MED   MED     1.000000
ET    ET      1.000000
BTI   BTI     1.000000
ABBV  ABBV    1.000000
T     T       1.000000
DFS   DFS     1.000000
BEP   BEP     1.000000
PEAK  PEAK    1.000000
DUK   DUK     1.000000
NTES  NTES    1.000000
INTC  INTC    1.000000
JNJ   JNJ     1.000000
BMY   BMY     1.000000
DUK   PEAK    0.615049
PEAK  DUK     0.615049
MCD   DFS     0.584761
DFS   MCD     0.584761
      T       0.538436
T     DFS     0.538436
NVS   JNJ     0.532011
JNJ   NVS     0.532011
T     DUK     0.528277
DUK   T       0.528277
PEAK  DFS     0.522560
DFS   PEAK    0.522560
DUK   BEP     0.517638
BEP   DUK     0.517638
DUK   JNJ     0.515478
                ...   
NTES  MED     0.198824
MED   NTES    0.198824
      BTI     0.185314
BTI   MED     0.185314
JNJ   MED     0.184951
MED   JNJ     0.184951
T     NTES    0.181377
NTES  T       0.181377
MED   BMY     0.176019
BMY   MED     0.176019
MED   NVS     0.175026
NVS   MED     0.175026
MED   DUK     0.170286
DUK   MED     0.170286
MED   ABBV    0.169227
ABBV  MED     0.169227
JNJ   ET      0.167954
ET    JNJ     0.167954
NTES  PEAK    0.157801
PEAK  NTES    0.157801
ET    BMY     0.156688
BMY   ET      0.156688
BEP   NTES    0.153414
NTES  BEP     0.153414
MED   ET      0.148256
ET    MED     0.148256
DUK   NTES    0.135846
NTES  DUK     0.135846
DUK   ET      0.132593
ET    DUK     0.132593
Length: 225, dtype: float64

print("Stock returns: ")
print(rets.mean())
print('-' * 50)
print("Stock risks:")
print(rets.std())

Stock returns: 
BMY     0.000130
MED     0.001932
ET      0.000441
BTI     0.000065
ABBV    0.000825
T       0.000201
DFS     0.000439
JNJ     0.000481
BEP     0.001008
PEAK    0.000203
DUK     0.000371
NTES    0.001217
INTC    0.000802
MCD     0.000607
NVS     0.000381
dtype: float64
--------------------------------------------------
Stock risks:
BMY     0.017037
MED     0.030629
ET      0.035920
BTI     0.016956
ABBV    0.018688
T       0.014743
DFS     0.026557
JNJ     0.012727
BEP     0.017715
PEAK    0.021118
DUK     0.014241
NTES    0.025516
INTC    0.020660
MCD     0.014915
NVS     0.012697
dtype: float64

table = pd.DataFrame()
table['Returns'] = rets.mean()
table['Risk'] = rets.std()
table.sort_values(by='Returns')

	Returns	Risk
BTI	0.000065	0.016956
BMY	0.000130	0.017037
T	0.000201	0.014743
PEAK	0.000203	0.021118
DUK	0.000371	0.014241
NVS	0.000381	0.012697
DFS	0.000439	0.026557
ET	0.000441	0.035920
JNJ	0.000481	0.012727
MCD	0.000607	0.014915
INTC	0.000802	0.020660
ABBV	0.000825	0.018688
BEP	0.001008	0.017715
NTES	0.001217	0.025516
MED	0.001932	0.030629

table.sort_values(by='Risk')

	Returns	Risk
NVS	0.000381	0.012697
JNJ	0.000481	0.012727
DUK	0.000371	0.014241
T	0.000201	0.014743
MCD	0.000607	0.014915
BTI	0.000065	0.016956
BMY	0.000130	0.017037
BEP	0.001008	0.017715
ABBV	0.000825	0.018688
INTC	0.000802	0.020660
PEAK	0.000203	0.021118
NTES	0.001217	0.025516
DFS	0.000439	0.026557
MED	0.001932	0.030629
ET	0.000441	0.035920

rf = 0.01
table['Sharpe Ratio'] = (table['Returns'] - rf) / table['Risk']
table

	Returns	Risk	Sharpe Ratio
BMY	0.000130	0.017037	-0.579353
MED	0.001932	0.030629	-0.263418
ET	0.000441	0.035920	-0.266110
BTI	0.000065	0.016956	-0.585904
ABBV	0.000825	0.018688	-0.490961
T	0.000201	0.014743	-0.664674
DFS	0.000439	0.026557	-0.360010
JNJ	0.000481	0.012727	-0.747934
BEP	0.001008	0.017715	-0.507614
PEAK	0.000203	0.021118	-0.463927
DUK	0.000371	0.014241	-0.676146
NTES	0.001217	0.025516	-0.344223
INTC	0.000802	0.020660	-0.445200
MCD	0.000607	0.014915	-0.629777
NVS	0.000381	0.012697	-0.757553

table['Max Returns'] = rets.max()

table['Min Returns'] = rets.min()

table['Median Returns'] = rets.median()

total_return = stock_rets[-1:].transpose()
table['Total Return'] = 100 * total_return
table

	Returns	Risk	Sharpe Ratio	Max Returns	Min Returns	Median Returns	Total Return
BMY	0.000130	0.017037	-0.579353	0.061207	-0.159851	0.000983	0.727148
MED	0.001932	0.030629	-0.263418	0.308843	-0.286532	0.001797	6.536049
ET	0.000441	0.035920	-0.266110	0.339360	-0.419771	0.000000	-0.935831
BTI	0.000065	0.016956	-0.585904	0.110688	-0.088633	0.000189	3.067651
ABBV	0.000825	0.018688	-0.490961	0.137673	-0.162524	0.001234	1.944501
T	0.000201	0.014743	-0.664674	0.100223	-0.092410	0.000725	1.019712
DFS	0.000439	0.026557	-0.360010	0.267542	-0.268354	0.000698	2.460146
JNJ	0.000481	0.012727	-0.747934	0.079977	-0.100379	0.000530	-0.107287
BEP	0.001008	0.017715	-0.507614	0.166883	-0.143016	0.001194	1.492222
PEAK	0.000203	0.021118	-0.463927	0.148885	-0.228344	0.001345	2.308582
DUK	0.000371	0.014241	-0.676146	0.123009	-0.115021	0.000915	-1.267558
NTES	0.001217	0.025516	-0.344223	0.140844	-0.148030	0.001106	-0.320028
INTC	0.000802	0.020660	-0.445200	0.195213	-0.180415	0.001083	-0.981557
MCD	0.000607	0.014915	-0.629777	0.181254	-0.158753	0.000977	-0.830213
NVS	0.000381	0.012697	-0.757553	0.074794	-0.098247	0.000676	0.900896

table['Average Return Days'] = (1 + total_return)**(1 / days) - 1
table

	Returns	Risk	Sharpe Ratio	Max Returns	Min Returns	Median Returns	Total Return	Average Return Days
BMY	0.000130	0.017037	-0.579353	0.061207	-0.159851	0.000983	0.727148	4.434019e-06
MED	0.001932	0.030629	-0.263418	0.308843	-0.286532	0.001797	6.536049	3.874814e-05
ET	0.000441	0.035920	-0.266110	0.339360	-0.419771	0.000000	-0.935831	-5.754191e-06
BTI	0.000065	0.016956	-0.585904	0.110688	-0.088633	0.000189	3.067651	1.849184e-05
ABBV	0.000825	0.018688	-0.490961	0.137673	-0.162524	0.001234	1.944501	1.178610e-05
T	0.000201	0.014743	-0.664674	0.100223	-0.092410	0.000725	1.019712	6.209003e-06
DFS	0.000439	0.026557	-0.360010	0.267542	-0.268354	0.000698	2.460146	1.487386e-05
JNJ	0.000481	0.012727	-0.747934	0.079977	-0.100379	0.000530	-0.107287	-6.569455e-07
BEP	0.001008	0.017715	-0.507614	0.166883	-0.143016	0.001194	1.492222	9.064898e-06
PEAK	0.000203	0.021118	-0.463927	0.148885	-0.228344	0.001345	2.308582	1.396789e-05
DUK	0.000371	0.014241	-0.676146	0.123009	-0.115021	0.000915	-1.267558	-7.806949e-06
NTES	0.001217	0.025516	-0.344223	0.140844	-0.148030	0.001106	-0.320028	-1.961694e-06
INTC	0.000802	0.020660	-0.445200	0.195213	-0.180415	0.001083	-0.981557	-6.036736e-06
MCD	0.000607	0.014915	-0.629777	0.181254	-0.158753	0.000977	-0.830213	-5.102056e-06
NVS	0.000381	0.012697	-0.757553	0.074794	-0.098247	0.000676	0.900896	5.488766e-06

initial_value = df.iloc[0]
ending_value = df.iloc[-1]
table['CAGR'] = ((ending_value / initial_value) ** (252.0 / days)) -1
table

	Returns	Risk	Sharpe Ratio	Max Returns	Min Returns	Median Returns	Total Return	Average Return Days	CAGR
BMY	0.000130	0.017037	-0.579353	0.061207	-0.159851	0.000983	0.727148	4.434019e-06	-0.003237
MED	0.001932	0.030629	-0.263418	0.308843	-0.286532	0.001797	6.536049	3.874814e-05	0.288620
ET	0.000441	0.035920	-0.266110	0.339360	-0.419771	0.000000	-0.935831	-5.754191e-06	-0.039155
BTI	0.000065	0.016956	-0.585904	0.110688	-0.088633	0.000189	3.067651	1.849184e-05	-0.013614
ABBV	0.000825	0.018688	-0.490961	0.137673	-0.162524	0.001234	1.944501	1.178610e-05	0.119298
T	0.000201	0.014743	-0.664674	0.100223	-0.092410	0.000725	1.019712	6.209003e-06	0.015984
DFS	0.000439	0.026557	-0.360010	0.267542	-0.268354	0.000698	2.460146	1.487386e-05	0.013678
JNJ	0.000481	0.012727	-0.747934	0.079977	-0.100379	0.000530	-0.107287	-6.569455e-07	0.071976
BEP	0.001008	0.017715	-0.507614	0.166883	-0.143016	0.001194	1.492222	9.064898e-06	0.159327
PEAK	0.000203	0.021118	-0.463927	0.148885	-0.228344	0.001345	2.308582	1.396789e-05	-0.004463
DUK	0.000371	0.014241	-0.676146	0.123009	-0.115021	0.000915	-1.267558	-7.806949e-06	0.047909
NTES	0.001217	0.025516	-0.344223	0.140844	-0.148030	0.001106	-0.320028	-1.961694e-06	0.167445
INTC	0.000802	0.020660	-0.445200	0.195213	-0.180415	0.001083	-0.981557	-6.036736e-06	0.107753
MCD	0.000607	0.014915	-0.629777	0.181254	-0.158753	0.000977	-0.830213	-5.102056e-06	0.089968
NVS	0.000381	0.012697	-0.757553	0.074794	-0.098247	0.000676	0.900896	5.488766e-06	0.053626

table.sort_values(by='Average Return Days')

	Returns	Risk	Sharpe Ratio	Max Returns	Min Returns	Median Returns	Total Return	Average Return Days	CAGR
DUK	0.000371	0.014241	-0.676146	0.123009	-0.115021	0.000915	-1.267558	-7.806949e-06	0.047909
INTC	0.000802	0.020660	-0.445200	0.195213	-0.180415	0.001083	-0.981557	-6.036736e-06	0.107753
ET	0.000441	0.035920	-0.266110	0.339360	-0.419771	0.000000	-0.935831	-5.754191e-06	-0.039155
MCD	0.000607	0.014915	-0.629777	0.181254	-0.158753	0.000977	-0.830213	-5.102056e-06	0.089968
NTES	0.001217	0.025516	-0.344223	0.140844	-0.148030	0.001106	-0.320028	-1.961694e-06	0.167445
JNJ	0.000481	0.012727	-0.747934	0.079977	-0.100379	0.000530	-0.107287	-6.569455e-07	0.071976
BMY	0.000130	0.017037	-0.579353	0.061207	-0.159851	0.000983	0.727148	4.434019e-06	-0.003237
NVS	0.000381	0.012697	-0.757553	0.074794	-0.098247	0.000676	0.900896	5.488766e-06	0.053626
T	0.000201	0.014743	-0.664674	0.100223	-0.092410	0.000725	1.019712	6.209003e-06	0.015984
BEP	0.001008	0.017715	-0.507614	0.166883	-0.143016	0.001194	1.492222	9.064898e-06	0.159327
ABBV	0.000825	0.018688	-0.490961	0.137673	-0.162524	0.001234	1.944501	1.178610e-05	0.119298
PEAK	0.000203	0.021118	-0.463927	0.148885	-0.228344	0.001345	2.308582	1.396789e-05	-0.004463
DFS	0.000439	0.026557	-0.360010	0.267542	-0.268354	0.000698	2.460146	1.487386e-05	0.013678
BTI	0.000065	0.016956	-0.585904	0.110688	-0.088633	0.000189	3.067651	1.849184e-05	-0.013614
MED	0.001932	0.030629	-0.263418	0.308843	-0.286532	0.001797	6.536049	3.874814e-05	0.288620